The datasets underlying this network analysis were generated through searches of the Early Modern Letters Online database. View the dataset on men corresponding with women as a CSV here; on women corresponding with men as a CSV here; and women corresponding with women as a CSV here.

I selected a dataset focused on correspondence involving women within the early modern Republic of Letters, specifically in London between 1650 and 1670. I chose this specific period of history because I was able to examine correspondence networks during a time of intense political transition and urban crisis, including the Restoration, the Great Plague, and the Great Fire. These conditions likely influenced an influx of communication, making the network especially meaningful to analyze given the events that were occurring. By filtering the dataset to focus on gender, I wanted to better understand how women participated in these networks and whether they acted as central figures or more peripheral contributors.

The network visualization demonstrates a lack of balance in how individuals are connected. Most of the women in the dataset appear as smaller nodes with only one connection, which suggests that their participation often took place through more personal, one-to-one exchanges. On the contrary, a small number of individuals stand out as much larger nodes, including figures such as Richard Hubberthorne, Constantijn Huygens, and Alexander Parker, who have significantly higher numbers of connections than others in the network. These people are like the main connectors, helping different people talk to each other and keeping the communication going. Most of the important and well-connected people in the network are men, which means men were more likely to have key and powerful roles.

What stood out to me most is how uneven the network is. Women are definitely present throughout the network, but they’re usually not the ones sitting at the center of everything. Instead, a lot of them are connected through a few more highly connected male figures, rather than forming large networks on their own. They’re not completely gone, but there are definitely many smaller groups where women are still talking and making their own connections in their local areas. This network is less about women not participating, and more about how their participation shows up differently in the structure of the network.

Seeing this dataset visualized into a network makes it easier to understand as opposed to just looking at a spreadsheet. The colors and the node sizes make it easily obvious who the most central figures are and how people are connected, without having to filter through rows of data and figuring out who overlaps. You can see names instead of patterns, and figure out who is acting as a hub and who is a part of smaller isolated exchanges. This helps to highlight how communication was occurring across distance and also shows that while women were part of these networks, they were not always driving the biggest connections.